code: def test_in_gridsearch(scheduler, xy_classification):
      X, y = xy_classification
      clf = Incremental(SGDClassifier(random_state=0, tol=1e-3))
      param_grid = {"estimator__alpha": [0.1, 10]}
      gs = sklearn.model_selection.GridSearchCV(clf, param_grid, cv=3)
  
      with scheduler() as (s, [a, b]):
          gs.fit(X, y, classes=[0, 1])
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  import sklearn.model_selection
  from dask.array.utils import assert_eq
  from scipy.sparse import csr_matrix
  from sklearn.base import clone
  from sklearn.linear_model import SGDClassifier, SGDRegressor
  from sklearn.pipeline import make_pipeline
  import dask_ml.feature_extraction.text
  import dask_ml.metrics
  from dask_ml.metrics.scorer import check_scoring
  from dask_ml.wrappers import Incremental
label_desc: Use image node_id 4 for calling a global function with example usage: test_in_gridsearch(scheduler, xy_classification) without return types